importpandas as pdimportnumpy as npfromsklearn.model_selectionimportStratifiedShuffleSplit#主要用于label分布不均匀的样本中fromsklearn.feature_selectionimportVarianceThreshold, SelectFromModel#第一个是特征选择中的方差阈值法(设定一个阈值,小于这个阈值就丢弃),第二个是嵌入式特征选择的一种#from sklearn.preproce...
import numpy as np import matplotlib.pyplot as plt from sklearn.learning_curve import learning_curve # 用sklearn的learning_curve得到training_score和cv_score,使用matplotlib画出learning curve def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1, train_sizes=np.linspace(...
from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_wine 1. 2. 3. 4. 导入需要的数据集 wine = load_wine() wine.data wine.target 1. 2. 3. 复习:sklearn建模基本流程 from sklearn.model_selection import train_test...
dtreeviz A Python 3 library for sci-kit learn, XGBoost, LightGBM, Spark, and TensorFlow decision tree visualization 14 node Building data structures as node trees 14 sahi A vision library for performing sliced inference on large images/small objects 14 pyre-extensions Type system extensions for use...
Drawing a waterfall plot - using the Tree Ensemble example - fails when using the RandomForestRegressor: # Reproducible error, code taken from: https://github.com/slundberg/shap with modification in line 6 import sklearn import xgboost import shap # train a Random Forest model X, y = shap...
XGBoost30,85 stands for “Extreme Gradient Boosting” and it is a variant of the gradient boosting machine which uses a more regularized model formalization to control overfitting. Fig. 7 Parallel coordinates plot from data subset 10. The mean of each predictor is set to zero and the ...
Several hyperparameters need to be tuned, including the number of trees, the size of each tree and the learning rate. Can overfit the training set if not properly regularized or if the number of boosting iterations is too high. Prediction can be slower compared to other models, as it requir...
TMAP: A new data visualization method, TMAP, capable of representing data sets of up to millions of data points and arbitrary high dimensionality as a two-dimensional tree. Visualizations based on TMAP are better suited than t-SNE or UMAP for the exploration and interpretation of large data set...
from sklearn.tree import DecisionTreeClassifier from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import GradientBoostingClassifier from xgboost import XGBClassifier,XGBRegressor from catboost import CatBoostClassifier,CatBoostRegressor from sklearn.ensemble import RandomForestClassifier,RandomForestReg...
模型我采用了DT/RF/GBDT/SVC,由于xgboost输出是概率,需要指定阈值确定0/1,可能我指定不恰当,效果不好0.78847。 效果最好的是RF,0.81340。这里经过筛选我使用的特征包括’Pclass’,’Gender’, ‘Cabin’,’Ticket’,’Embarked’,’Title’进行onehot编码,’Age’,’SibSp’,’Parch’,’Fare’,’class_age’...